Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: ML Alignment Theory Program under Evan Hubinger, published by Oliver Zhang on December 6, 2021 on The AI Alignment Forum.

In the past six weeks, the Stanford Existential Risks Initiative (SERI) has been running a work trial for the “ML Alignment Theory Scholars” (MATS) program. Our goal is to increase the number of people working on alignment theory, and to do this, we’re running a scholars program that provides mentorship, funding, and community to promising new alignment theorists. This program is run in partnership with Evan Hubinger, who has been providing all of the mentorship to each of the scholars for their work trial.

As the final phase of the work trial, each participant has taken a previous research artifact (usually an Alignment Forum post) and written a distillation and expansion of that post. The posts were picked by Evan and each participant signed up for one they were interested in. Within the next two weeks (12/7 - 12/17), we’ll be posting all of these posts to lesswrong and the alignment forum as part of a sequence, with a couple of posts going up each day. (There will be around 10-15 posts total.)

Community Engagement

Evan will be evaluating each post to determine whether participants make it to the next stage of the program (where they have the opportunity to do novel research with a mentor), but we’d also be interested in hearing community feedback on each post. This could be just through upvotes or alternatively, via comments as well. We’ll run a conclusion post with our takeaways a week or two after the final blogpost has been released. If it’s interesting, we’d also be happy to post the story of MATS’ creation for other prospective community builders.

Additionally, if Evan knows you and you would be interested in mentoring one of the participants for the next stage of the program—e.g. you really liked their post and think it would be productive to work with them—you should reach out to Evan.

Program Description

From here on out, we’ll be discussing our program and future strategy. The previous two paragraphs are all the context needed to understand the series of blog posts.

The SERI MATS program aims to gather promising alignment theorists and give them an opportunity to work under an experienced mentor.

The application process was as follows. We first asked the mentors of the Cambridge AGI safety fundamentals for promising participants and interviewed them. After the interviews, we put the scholars through a paid, six-week long work trial for 10 hours a week. During these times, scholars read through Evan’s reading list and distilled and expanded one of the research artifacts that Evan chose. Evan will be evaluating each final blogpost to determine who gets into the scholars program.

The scholars program will initially be a two-month program in which scholars work on one of the problems in Evan’s list of subproblems. Scholars have the option of forming teams. If they perform good research (as evaluated by Evan), we hope to be able to repeat the program indefinitely beyond the initial two-month program.

Future Steps

In the immediate future, we will be looking to continue to refine the program and also expand to more mentors beyond Evan. Additionally, we’ll likely be running another round of this program this spring or summer, though we are not yet looking for applications.

Our long-term goal would be to create a self-sustaining community of alignment theorist mentees and mentors which helps build up new theorists and connect them to mentors, specific subproblems, and (hopefully) research organizations. In other words, we’re trying to facilitate the transition between having finished a reading group like AGI safety fundamentals and working on the problems as a part-time or full-time job.

Acknowledgements

We’re very grateful to be supported by a...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Interpreting Yudkowsky on Deep vs Shallow Knowledge, published by adamShimi on December 5, 2021 on LessWrong.

Here is an exploration of what Eliezer Yudkowsky means when he writes about deep vs shallow patterns (although I’ll be using "knowledge" instead of "pattern" for reasons explained in the next section). Not about any specific pattern Yudkowsky is discussing, mind you, about what deep and shallow patterns are at all. In doing so, I don’t make any criticism of his ideas and instead focus on quoting him (seriously, this post is like 70% quotes) and interpreting him by finding the best explanation I can of his words (that still fit them, obviously). Still, there’s a risk that my interpretation misses some of his points and ideas— I’m building a lower-bound on his argument’s power that is as high as I can get, not an upper-bound. Also, I might just be completely wrong, in which case defer to Yudkowsky if he points out that I’m completely missing the point.

Thanks to Eliezer Yudkowsky, Steve Byrnes, John Wentworth, Connor Leahy, Richard Ngo, Kyle, Laria, Alex Turner, Daniel Kokotajlo and Logan Smith for helpful comments on a draft.

Back to the FOOM: Yudkowsky’s explanation

In recent discussions, Yudkowsky often talks about deep patterns and deep thinking. What he made clear in a comment on this draft is that he has been using the term “deep patterns” in two different ways:

What I’ll call deep knowledge, which is a form of human knowledge/theory as well as the related epistemic strategies. This is what I explore below.

What I’ll call deep cognition, which is the sort of deep patterns that Yudkowsky points out AGI would have. There’s a link and an analogy with the deep knowledge, but I don’t get it enough to write something convincing to me and Yudkowsky, so I’ll mostly avoid that topic in this post.

Focusing on deep knowledge then, Yudkowsky recently seems to ascribe his interlocutors’ failure to grasp his point to their inability to grasp different instances of deep knowledge.

(All quotes from Yudkowsky if not mentioned otherwise)

(From the first discussion with Richard Ngo)

In particular, just as I have a model of the Other Person's Beliefs in which they think alignment is easy because they don't know about difficulties I see as very deep and fundamental and hard to avoid, I also have a model in which people think "why not just build an AI which does X but not Y?" because they don't realize what X and Y have in common, which is something that draws deeply on having deep models of intelligence. And it is hard to convey this deep theoretical grasp.

That being said, he doesn’t really explain what this sort of deep knowledge is.

(From the same discussion with Ngo)

(Though it's something of a restatement, a reason I'm not going into "my intuitions about how cognition works" is that past experience has led me to believe that conveying this info in a form that the Other Mind will actually absorb and operate, is really quite hard and takes a long discussion, relative to my current abilities to Actually Explain things; it is the sort of thing that might take doing homework exercises to grasp how one structure is appearing in many places, as opposed to just being flatly told that to no avail, and I have not figured out the homework exercises.)

The thing is, he did exactly that in the FOOM debate with Robin Hanson 13 years ago. (For those unaware of this debate, Yudkoswky is responding to Hanson’s use of trends — like Moore’s law — extrapolations to think about intelligence explosion).

(From The Weak Inside View (2008))

Robin keeps asking me what I’m getting at by talking about some reasoning as “deep” while other reasoning is supposed to be “surface.” One thing which makes me worry that something is “surface” is when it involves generalizing a level N feature across a shift ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Interpreting Yudkowsky on Deep vs Shallow Knowledge, published by adamShimi on December 5, 2021 on LessWrong.

Here is an exploration of what Eliezer Yudkowsky means when he writes about deep vs shallow patterns (although I’ll be using "knowledge" instead of "pattern" for reasons explained in the next section). Not about any specific pattern Yudkowsky is discussing, mind you, about what deep and shallow patterns are at all. In doing so, I don’t make any criticism of his ideas and instead focus on quoting him (seriously, this post is like 70% quotes) and interpreting him by finding the best explanation I can of his words (that still fit them, obviously). Still, there’s a risk that my interpretation misses some of his points and ideas— I’m building a lower-bound on his argument’s power that is as high as I can get, not an upper-bound. Also, I might just be completely wrong, in which case defer to Yudkowsky if he points out that I’m completely missing the point.

Thanks to Eliezer Yudkowsky, Steve Byrnes, John Wentworth, Connor Leahy, Richard Ngo, Kyle, Laria, Alex Turner, Daniel Kokotajlo and Logan Smith for helpful comments on a draft.

Back to the FOOM: Yudkowsky’s explanation

In recent discussions, Yudkowsky often talks about deep patterns and deep thinking. What he made clear in a comment on this draft is that he has been using the term “deep patterns” in two different ways:

What I’ll call deep knowledge, which is a form of human knowledge/theory as well as the related epistemic strategies. This is what I explore below.

What I’ll call deep cognition, which is the sort of deep patterns that Yudkowsky points out AGI would have. There’s a link and an analogy with the deep knowledge, but I don’t get it enough to write something convincing to me and Yudkowsky, so I’ll mostly avoid that topic in this post.

Focusing on deep knowledge then, Yudkowsky recently seems to ascribe his interlocutors’ failure to grasp his point to their inability to grasp different instances of deep knowledge.

(All quotes from Yudkowsky if not mentioned otherwise)

(From the first discussion with Richard Ngo)

In particular, just as I have a model of the Other Person's Beliefs in which they think alignment is easy because they don't know about difficulties I see as very deep and fundamental and hard to avoid, I also have a model in which people think "why not just build an AI which does X but not Y?" because they don't realize what X and Y have in common, which is something that draws deeply on having deep models of intelligence. And it is hard to convey this deep theoretical grasp.

That being said, he doesn’t really explain what this sort of deep knowledge is.

(From the same discussion with Ngo)

(Though it's something of a restatement, a reason I'm not going into "my intuitions about how cognition works" is that past experience has led me to believe that conveying this info in a form that the Other Mind will actually absorb and operate, is really quite hard and takes a long discussion, relative to my current abilities to Actually Explain things; it is the sort of thing that might take doing homework exercises to grasp how one structure is appearing in many places, as opposed to just being flatly told that to no avail, and I have not figured out the homework exercises.)

The thing is, he did exactly that in the FOOM debate with Robin Hanson 13 years ago. (For those unaware of this debate, Yudkoswky is responding to Hanson’s use of trends — like Moore’s law — extrapolations to think about intelligence explosion).

(From The Weak Inside View (2008))

Robin keeps asking me what I’m getting at by talking about some reasoning as “deep” while other reasoning is supposed to be “surface.” One thing which makes me worry that something is “surface” is when it involves generalizing a level N feature across a shift ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Behavior Cloning is Miscalibrated, published by leogao on December 5, 2021 on LessWrong.

Behavior cloning (BC) is, put simply, when you have a bunch of human expert demonstrations and you train your policy to maximize likelihood over the human expert demonstrations. It’s the simplest possible approach under the broader umbrella of Imitation Learning, which also includes more complicated things like Inverse Reinforcement Learning or Generative Adversarial Imitation Learning. Despite its simplicity, it’s a fairly strong baseline. In fact, prompting GPT-3 to act agent-y is essentially also BC, just rather than cloning on a specific task, you're cloning against all of the task demonstration-like data in the training set--but fundamentally, it's a scaled up version of the exact same thing. The problem with BC that leads to miscalibration is that the human demonstrator may know more or less than the model, which would result in the model systematically being over/underconfident for its own knowledge and abilities.

For instance, suppose the human demonstrator is more knowledgeable than the model at common sense: then, the human will ask questions about common sense much less frequently than the model should. However, with BC, the model will ask those questions at the exact same rate as the human, and then because now it has strictly less information than the human, it will have to marginalize over the possible values of the unobserved variables using its prior to be able to imitate the human’s actions. Factoring out the model’s prior over unobserved information, this is equivalent[1] to taking a guess at the remaining relevant info conditioned on all the other info it has (!!!), and then act as confidently as if it had actually observed that info, since that's how a human would act (since the human really observed that information outside of the episode, but our model has no way of knowing that).

This is, needless to say, a really bad thing for safety; we want our models to ask us or otherwise seek out information whenever they don't know something, not randomly hallucinate facts.

In theory we could fix this by providing enough information in the context such that the human doesn’t know anything the model doesn’t also know. However, this is very impractical in the real world, because of how much information is implicit in many interactions. The worst thing about this, though, is that it fails silently. Even if we try our best to supply the model with all the information we think it needs, if we forget anything the model won't do anything to let us know; instead, it will silently roll the dice and then pretend nothing ever happened.

The reverse can also happen, where if the model knows more than the human, then it will collect a bunch of unnecessary info which it discards so that its decision is as dumb as the human's. This is generally not as dangerous, though it might still mislead us to the capabilities of the model (we might think it's less knowledgeable than it actually is), and it would use resources suboptimally, so it's still best avoided. Also, the model might do both at the same time in different domains of knowledge, if the human is more knowledgeable in one area but less in another.

Plus, you don’t even need unobserved information in the information-theoretic sense. If your agent has more logical uncertainty than the human, you end up with the exact same problem; for example if it’s significantly better/worse at mental math than the human in an environment where the human/agent can choose to use a calculator provided as part of the environment that costs some small amount of reward to use, even though the agent has access to the exact same info as the human, it will choose to use a calculator too often/not often enough.

This isn’t a purely theoretical problem. Th...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Behavior Cloning is Miscalibrated, published by leogao on December 5, 2021 on LessWrong.

Behavior cloning (BC) is, put simply, when you have a bunch of human expert demonstrations and you train your policy to maximize likelihood over the human expert demonstrations. It’s the simplest possible approach under the broader umbrella of Imitation Learning, which also includes more complicated things like Inverse Reinforcement Learning or Generative Adversarial Imitation Learning. Despite its simplicity, it’s a fairly strong baseline. In fact, prompting GPT-3 to act agent-y is essentially also BC, just rather than cloning on a specific task, you're cloning against all of the task demonstration-like data in the training set--but fundamentally, it's a scaled up version of the exact same thing. The problem with BC that leads to miscalibration is that the human demonstrator may know more or less than the model, which would result in the model systematically being over/underconfident for its own knowledge and abilities.

For instance, suppose the human demonstrator is more knowledgeable than the model at common sense: then, the human will ask questions about common sense much less frequently than the model should. However, with BC, the model will ask those questions at the exact same rate as the human, and then because now it has strictly less information than the human, it will have to marginalize over the possible values of the unobserved variables using its prior to be able to imitate the human’s actions. Factoring out the model’s prior over unobserved information, this is equivalent[1] to taking a guess at the remaining relevant info conditioned on all the other info it has (!!!), and then act as confidently as if it had actually observed that info, since that's how a human would act (since the human really observed that information outside of the episode, but our model has no way of knowing that).

This is, needless to say, a really bad thing for safety; we want our models to ask us or otherwise seek out information whenever they don't know something, not randomly hallucinate facts.

In theory we could fix this by providing enough information in the context such that the human doesn’t know anything the model doesn’t also know. However, this is very impractical in the real world, because of how much information is implicit in many interactions. The worst thing about this, though, is that it fails silently. Even if we try our best to supply the model with all the information we think it needs, if we forget anything the model won't do anything to let us know; instead, it will silently roll the dice and then pretend nothing ever happened.

The reverse can also happen, where if the model knows more than the human, then it will collect a bunch of unnecessary info which it discards so that its decision is as dumb as the human's. This is generally not as dangerous, though it might still mislead us to the capabilities of the model (we might think it's less knowledgeable than it actually is), and it would use resources suboptimally, so it's still best avoided. Also, the model might do both at the same time in different domains of knowledge, if the human is more knowledgeable in one area but less in another.

Plus, you don’t even need unobserved information in the information-theoretic sense. If your agent has more logical uncertainty than the human, you end up with the exact same problem; for example if it’s significantly better/worse at mental math than the human in an environment where the human/agent can choose to use a calculator provided as part of the environment that costs some small amount of reward to use, even though the agent has access to the exact same info as the human, it will choose to use a calculator too often/not often enough.

This isn’t a purely theoretical problem. Th...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What have your romantic experiences with non-EAs/non-Rationalists been like?, published by Randomized, Controlled on December 5, 2021 on LessWrong.

Epistemic status: trawlin' for anecdotes/commiserations; satiating-morbid-curiosity

I'm also interested in non-romantic relationships, but I'm particularly interested in hearing about long term relationships between folks within EA and/or LW and someone[s] outside it.

By outside of EA/Rat I don't just mean "not active in the communities". I mean: doesn't have the philosophical machinery of rationality [ethics/meta-ethics/epistemology/cog sci/etc] and doesn't seem inclined to pick it up.

Things that I'm particularly curious about:

how long these relationships lasted

how you feel about the relationship: in what ways was/is it successful or unsuccessful?

if your partner doesn't have a "rationalist frame", how you deal with that

any other random thoughts this elicits

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What have your romantic experiences with non-EAs/non-Rationalists been like?, published by Randomized, Controlled on December 5, 2021 on LessWrong.

Epistemic status: trawlin' for anecdotes/commiserations; satiating-morbid-curiosity

I'm also interested in non-romantic relationships, but I'm particularly interested in hearing about long term relationships between folks within EA and/or LW and someone[s] outside it.

By outside of EA/Rat I don't just mean "not active in the communities". I mean: doesn't have the philosophical machinery of rationality [ethics/meta-ethics/epistemology/cog sci/etc] and doesn't seem inclined to pick it up.

Things that I'm particularly curious about:

how long these relationships lasted

how you feel about the relationship: in what ways was/is it successful or unsuccessful?

if your partner doesn't have a "rationalist frame", how you deal with that

any other random thoughts this elicits

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What “defense layers” should governments, AI labs, and businesses use to prevent catastrophic AI failures?, published by alexlintz on December 3, 2021 on The Effective Altruism Forum.

Rethink Priorities is working on a project called ‘Defense in Depth Against Catastrophic AI Failures’. “Defense in depth” refers to the use of multiple redundant layers of safety and/or security measures such that each layer reduces the chance of catastrophe. Our project is intended to (1) make the case for taking a defense in depth approach to ensuring safety when deploying near-term, high-stakes AI systems and (2) identify many defense layers/measures that may be useful for this purpose.

If you can think of any possible layers, please mention them below. We’re hoping to collect a very long list of such layers, either for inclusion in our main output or for potentially investigating further in future, so please err on the side of commenting even if the ideas are quite speculative, may not actually be useful, or may be things we’ve already thought of. Any relevant writing you can refer us to would also be useful.

If we end up including layers you suggest in our outputs, we’d be happy to either leave you anonymous or credit you, depending on your preference.

Some further info about the project: By “catastrophic AI failure”, we mean harmful accidents or harmful unintended use of computer systems that perform tasks typically associated with intelligent behavior (and especially of machine learning systems) that lead to at least 100 fatalities or $1 billion in economic loss. This could include failures in contexts like power grid management, autonomous weapons, or cyber offense (if you’re interested in more concrete examples, see here )

Defense layers can relate to any phase of a technology’s development and deployment, from early development to monitoring deployment to learning from failures, and can be about personnel, procedures, institutional set up, technical standards, etc.

Some examples of defense layers for AI include (find more here):

Procedures for vetting and deciding on institutional partners, investors, etc.

Methods for scaling human supervision and feedback during and after training high-stakes ML systems

Tools for blocking unauthorized use of developed/trained IP, akin to the PALs on nuclear weapons

Technical methods and process methods (e.g. certification; Cihon et al. 2021, benchmarks?) for gaining high confidence in certain properties of ML systems, and properties of the inputs to ML systems (e.g. datasets), at all stages of development (a la Ashmore et al. 2019)

Background checks & similar for people being hired or promoted to certain types of roles

Methods for avoiding or detecting supply chain attacks

Procedures for deciding when and how to engage one's host government to help with security/etc.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What “defense layers” should governments, AI labs, and businesses use to prevent catastrophic AI failures?, published by alexlintz on December 3, 2021 on The Effective Altruism Forum.

Rethink Priorities is working on a project called ‘Defense in Depth Against Catastrophic AI Failures’. “Defense in depth” refers to the use of multiple redundant layers of safety and/or security measures such that each layer reduces the chance of catastrophe. Our project is intended to (1) make the case for taking a defense in depth approach to ensuring safety when deploying near-term, high-stakes AI systems and (2) identify many defense layers/measures that may be useful for this purpose.

If you can think of any possible layers, please mention them below. We’re hoping to collect a very long list of such layers, either for inclusion in our main output or for potentially investigating further in future, so please err on the side of commenting even if the ideas are quite speculative, may not actually be useful, or may be things we’ve already thought of. Any relevant writing you can refer us to would also be useful.

If we end up including layers you suggest in our outputs, we’d be happy to either leave you anonymous or credit you, depending on your preference.

Some further info about the project: By “catastrophic AI failure”, we mean harmful accidents or harmful unintended use of computer systems that perform tasks typically associated with intelligent behavior (and especially of machine learning systems) that lead to at least 100 fatalities or $1 billion in economic loss. This could include failures in contexts like power grid management, autonomous weapons, or cyber offense (if you’re interested in more concrete examples, see here )

Defense layers can relate to any phase of a technology’s development and deployment, from early development to monitoring deployment to learning from failures, and can be about personnel, procedures, institutional set up, technical standards, etc.

Some examples of defense layers for AI include (find more here):

Procedures for vetting and deciding on institutional partners, investors, etc.

Methods for scaling human supervision and feedback during and after training high-stakes ML systems

Tools for blocking unauthorized use of developed/trained IP, akin to the PALs on nuclear weapons

Technical methods and process methods (e.g. certification; Cihon et al. 2021, benchmarks?) for gaining high confidence in certain properties of ML systems, and properties of the inputs to ML systems (e.g. datasets), at all stages of development (a la Ashmore et al. 2019)

Background checks & similar for people being hired or promoted to certain types of roles

Methods for avoiding or detecting supply chain attacks

Procedures for deciding when and how to engage one's host government to help with security/etc.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Important ideas for prioritizing ambitious funding opportunities, published by jh on December 3, 2021 on The Effective Altruism Forum.

At the recent EA Global, Will MacAskill called for the community to be ambitious and aim for non-marginal impacts. Ben Todd of 80,000 hours has also made ambition a theme, especially in regards to career choice. In this post, I develop some key ideas that seem important for prioritizing ambitious funding opportunities.

If you care about the social impact of your funding then you need to care about how other funders react to your actions. Scale, Neglectedness, Solvability (SNS) framing, at least as usually explained, doesn't account for such considerations. This could be an issue now that EA wants to pursue ambitious projects that will overlap with more stakeholders.

To address this issue, I have developed a model for impact in equilibrium. In the past I’ve only discussed this with technical audiences. For this post I've put together diagrams to explain the key ideas.

The main points are:

Scalability. That is, how much the effectiveness of a funding opportunity, whether nonprofit or for profit, persists with scale. Funding GiveDirectly is an example of an opportunity that is regarded as scaling relatively well. Scalability is already an implicit part of Solvability in SNS, but it stands out on its own in general equilibrium.

Urgency versus patience. To determine the ‘urgency’ of giving now, we need to compare it to 'investing to give' to the best funding opportunity in the future. This determines how much you should value impacts now compared to financial returns. In turn, this is crucial to assessing the tradeoffs involved in impact investing. Differences in urgency between funders will determine who funds what and how much.

Risk. Risk is a key driver of for-profit enterprise size. If we allow for 'altruistic' risk too, then risk can also be a key consideration for nonprofit enterprises. Part of the impact of funding an opportunity is helping the market by sharing the risk.

New takes on neglectedness. New ways of looking at neglectedness emerge in equilibrium. 'Investment neglectedness' is a ratio that highlights that it is more impactful to fund a project when its existing funders are less wealthy. But, all else equal (given the same access to funding), it is a positive sign if a project has already been able to attract investment. 'Value neglectedness' is a ratio that highlights that your impact on a funding opportunity will be greater when you value its impacts more than other funders.

This post is long but I feel that is necessary to cover all the main ideas. I plan to cover specific applications of these concepts in shorter posts in the future.

All sorts of questions, comments, and feedback on this post are very welcome and would be appreciated.

Background

This post is based on my work at the Total Portfolio Project. Our goal is to help EAs and aligned investors prioritize the most impactful funding opportunities. Whether these opportunities are grants, 'investing to give' or impact investments. Projects that we've completed so far range from theoretical research (like in this post), to advising on high impact investment deals, to strategic research on balancing 'give now versus later' for new major EA funders.

We see many investment-related research topics that are important, neglected, and tractable. Especially for financial professionals and academics who have a deep enough understanding of EA thinking (and we aim to help build this community). Topics we see as neglected include ‘give now versus later’, patient philanthropy, altruistic leverage and mission correlation.

As a step towards getting EA-aligned ideas considered by leading financial economists, I’ve prepared these two working papers. The goal of this post is to prese...